Principal Component Analysis

Mindmap

What's that?

such that the greatest variance comes to lie on the 1st coordinate axis

the second greatest variance comes to lie on the 2nd coordinate axis

and so on

Why is that helpful?

if your data points are N dimensional and you use the new PCA N dimensional representation, you have the data points described in a coordinate system that better fits to the distribution of your data in the N dimensional space

if you desribe the data using only the first M « N dimensions, i.e., its projection on the first M principal axes, you compress your data!

How to compute it?

the principal components correspond to the Eigenvectors of the covariance matrix